TY - UNPB

T1 - A Multinomial Probit Model with Latent Factors

T2 - Identification and Interpretation without a Measurement System

AU - Piatek, Rémi

AU - Gensowski, Miriam

PY - 2017/7

Y1 - 2017/7

N2 - We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify an a priori structure on a few drivers of unobserved heterogeneity. One such example is the choice of combinations of two options, which we explore with real data on education and occupation pairs.

AB - We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify an a priori structure on a few drivers of unobserved heterogeneity. One such example is the choice of combinations of two options, which we explore with real data on education and occupation pairs.

KW - Faculty of Social Sciences

KW - multinomial probit

KW - latent factors

KW - Bayesian analysis

KW - marginal data augmentation

KW - educational choice

KW - occupational choice

KW - C11

KW - C25

KW - C35

M3 - Working paper

T3 - IZA Discussion Paper

BT - A Multinomial Probit Model with Latent Factors

ER -